Yang, Jinyi and Fan, Xiaohui and Wu, Xue-Bing and
Wang, Feige and Bian, Fuyan and Yang, Qian and
McGreer, Ian D. and Yi, Weimin and Jiang, Linhua and
Green, Richard and Yue, Minghao and Wang, Shu and
Li, Zefeng and Ding, Jiani and Dye, S. and Lawrence,
Andy (2017) Discovery of 16 new z ∼ 5.5 quasars: filling
in the redshift gap of quasar color selection.
Astronomical Journal, 153 (4). p. 184. ISSN 1538-3881
Access from the University of Nottingham repository:
http://eprints.nottingham.ac.uk/42339/1/pdf.pdf
Copyright and reuse:
The Nottingham ePrints service makes this work by researchers of the University of
Nottingham available open access under the following conditions.
This article is made available under the University of Nottingham End User licence and may
be reused according to the conditions of the licence. For more details see:
http://eprints.nottingham.ac.uk/end_user_agreement.pdf
A note on versions:
The version presented here may differ from the published version or from the version of
record. If you wish to cite this item you are advised to consult the publisher’s version. Please
see the repository url above for details on accessing the published version and note that
access may require a subscription.
For more information, please contact [email protected]
The Astronomical Journal, 153:184 (10pp), 2017 April
https://doi.org/10.3847/1538-3881/aa6577
© 2017. The American Astronomical Society. All rights reserved.
Discovery of 16 New z∼5.5 Quasars: Filling in the
Redshift Gap of Quasar Color Selection
Jinyi Yang1,2,3, Xiaohui Fan2,3, Xue-Bing Wu1,2, Feige Wang1,2,3, Fuyan Bian4,9, Qian Yang1,2, Ian D. McGreer3, Weimin Yi5,6,
Linhua Jiang2, Richard Green3, Minghao Yue1,3, Shu Wang1,2, Zefeng Li1, Jiani Ding3, Simon Dye7, and Andy Lawrence8
1
Department
2
of Astronomy, School of Physics, Peking University, Beijing 100871, China
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, China
3
Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA
4
Research School of Astronomy and Astrophysics, Australian National University, Weston Creek, ACT 2611, Australia
5
Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011, China
6
Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650011, China
7
School of Physics and Astronomy, Nottingham University, University Park, Nottingham, NG7 2RD, UK
8
Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, UK
Received 2016 October 23; revised 2017 February 20; accepted 2017 March 6; published 2017 March 30
Abstract
We present initial results from the first systematic survey of luminous z∼5.5 quasars. Quasars at z ∼ 5.5, the postreionization epoch, are crucial tools to explore the evolution of intergalactic medium, quasar evolution, and the
early super-massive black hole growth. However, it has been very challenging to select quasars at redshifts
5.3 „ z „ 5.7 using conventional color selections, due to their similar optical colors to late-type stars, especially M
dwarfs, resulting in a glaring redshift gap in quasar redshift distributions. We develop a new selection technique for
z ∼ 5.5 quasars based on optical, near-IR, and mid-IR photometric data from Sloan Digital Sky Survey (SDSS),
UKIRT InfraRed Deep Sky Surveys—Large Area Survey (ULAS), VISTA Hemisphere Survey (VHS), and Wide
Field Infrared Survey Explorer. From our pilot observations in the SDSS-ULAS/VHS area, we have discovered
15 new quasars at 5.3 „ z „ 5.7 and 6 new lower redshift quasars, with SDSS z band magnitude brighter than 20.5.
Including other two z ∼ 5.5 quasars already published in our previous work, we now construct a uniform quasar
sample at 5.3 „ z „ 5.7, with 17 quasars in a ∼4800 square degree survey area. For further application in a larger
survey area, we apply our selection pipeline to do a test selection by using the new wide field J-band photometric
data from a preliminary version of the UKIRT Hemisphere Survey (UHS). We successfully discover the first UHS
selected z ∼ 5.5 quasar.
Key words: galaxies: active – galaxies: high-redshift – quasars: emission lines – quasars: general
(e.g., Bolton et al. 2012). They place strong constraints on
reionization topology, as well as on the sources of reionization
and chemical feedback by early galaxy population. Lyα
opacity measurement directly probes the evolution of IGM.
Following Fan et al. (2006), several new measurements about
the Lyα opacity at 5 < z < 6 are given (Simpson et al. 2014;
Becker et al. 2015), but IGM statistics are still poorly
constrained at z>5 (Becker et al. 2015).
Moreover, a quasar sample in this redshift range is also a key
to study the evolution of quasar luminosity function (QLF) and
black hole growth. At high redshift, the QLF and black hole
evolution have been measured at z ∼ 5 and 6 (Jiang et al. 2008;
Willott et al. 2010a, 2010b; McGreer et al. 2013; Kashikawa
et al. 2015; Yang et al. 2016). However, at z ∼ 5.5 they are still
poorly measured due to the lack of a complete quasar sample.
McGreer et al. (2013) derived the quasar spatial density at
z ∼ 4, 4.9, and 6, and fitted a luminosity-dependent density
evolution model to the combined data set. They concluded that
the quasar number density evolution steepens at high redshift,
such that luminous quasars decline as a population more
steeply at z > 5 than from z=4 to z=5 (also Jiang
et al. 2016). However, the exact evolution of quasar density
from z=5 to 6 is unclear because of the small size and high
incompleteness of the existing z ∼ 5.5 quasar sample. The
quasar number density at z∼5.5 is also needed to estimate the
contribution of quasars to the ionizing background just after
the reionization epoch. Willott et al. (2010a) suggested that
there was a rapid black hole mass growth phase after z ∼ 6.
1. Introduction
High redshift (z>5) quasars are important tracers to study
the early Universe. However, they are difficult to be found, due
to both low spatial density and high contaminants from cool
dwarfs when using color selection. Although more than
300,000 quasars are now known (e.g., Schneider et al. 2010;
Pâris et al. 2014, 2016), only ∼290 quasars are at z > 5. In the
distribution of quasar redshift, there is an obvious gap of
known quasars at 5.3 < z < 5.7, due to their similar optical
colors to that of late-type stars (see the redshift distribution in
Section 4). Only ∼30 known quasars have been found in this
redshift gap over a wide magnitude range (17.5 < z < 26 mag;
e.g., Stern et al. 2000; Romani et al. 2004; Cool et al. 2006;
Douglas et al. 2007; Matute et al. 2013; Bañados et al. 2016).
Compared with the studies at lower redshift and higher redshift,
this gap posts significant limit on the study of quasar evolution
from z ∼ 5 to 6, over the post-reionization epoch.
Observations of the Gunn-Peterson effect using absorption
spectra of high redshift quasars suggest that reionization is just
completing at z∼6, possibly with a tail to z∼5.5 (Fan et al.
2006; Becker et al. 2015; McGreer et al. 2015). Therefore, the
physical conditions of the post-reionization IGM, at z ∼ 5–6,
provides the basic boundary conditions of models of reionization, such as the evolution of IGM temperature, photon mean
free path, metallicity, and the impact of helium reionization
9
Stromlo Fellow.
1
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
Study of black hole growth at z ∼ 4.8 supports the notion of
fast SMBH growth at this epoch, corresponding to likely the
first such phase for most SMBH (Trakhtenbrot et al. 2011).
Studying BH growth properties at z∼5.5 will fill in the
missing link between z∼5 and 6.
To answer the questions posted previously, a large,
uniformly selected sample of quasars at 5.3<z<5.7 is
needed. However, so far there has not ever been a complete
quasar survey at z∼5.5. As shown in Section 2, broadband
colors of z∼5.5 quasars are very similar to those of much
more numerous M dwarfs, when a small number of passbands
are used. Therefore, to avoid the lager number of star
contaminations, previous quasar selections have always
excluded the region of M dwarf locus in r−i/i−z color–
color diagram. As a result, most surveys of high redshift
quasars have avoided the color space occupied by z∼5.5
quasars and are highly incomplete at this redshift. To construct
a large uniform z ∼ 5.5 quasar sample, a more effective
selection to separate quasars from M dwarf in this most
contaminated region is required.
In this paper, we report initial results from a new search that
focuses on the selection of z∼5.5 quasars. Our new color
selection criteria based on optical, near-, and mid-IR colors have
yielded 17 quasars in the redshift range of 5.3 „ z „ 5.7 during
the pilot observation described here. Our optical/IR color
selection technique and candidate selection using a combination
of existing and new imaging surveys are described in Section 2.
The details of our spectroscopy observations and new discoveries
are presented in Sections 3 and 4. In Section 5, we discuss the
completeness of our new selection and also report a test selection
and first discovery using the preliminary version of the UKIRT
Hemisphere Survey (UHS) photometric data. A summary is given
in Section 6. In this paper, we adopt a ΛCDM cosmology with
parameters WL = 0.728, Ωm=0.272, Ωb=0.0456, and
H0=70 km s-1 Mpc-1 (Komatsu et al. 2009). Photometric data
from the Sloan Digital Sky Survey (SDSS) are in the SDSS
photometric system (Lupton et al. 1999), which is almost identical
to the AB system at bright magnitudes; photometric data from IR
surveys are in the Vega system. All SDSS data shown in this
paper are corrected for Galactic extinction.
Figure 1. Top: color track of quasar at z=5 to 6 (red dots and line) with a step
of Δ z=0.1, generated by calculating the mean colors of simulated quasars at
each redshift bin. The simulated quasar sample used here is the same sample
described in Section 5.1. The contours show the locus of M dwarfs, from early
type to late type. The cyan contours denote M1–M3 dwarfs, the orange
contours denote M4–M6 dwarfs, and the purple contours denote M7–M9
dwarfs. Clearly, z∼5.5 quasars are serious, contaminated by late-type M
dwarfs. Bottom: the spectrum of an average of simulated z ∼ 5.5 quasars
compared with the spectrum of a typical M5 dwarf (http://dwarfarchives.org).
The dashed lines represent normalized SDSS r, i, and z bandpasses from left to
right. For the comparison, we scaled both the quasar spectrum and M dwarf
spectrum to i = 20.0. In this case, the synthetic SDSS r and z band magnitudes
of quasars are 22.1 and 18.8. For M5 dwarf, the magnitudes are 21.8 in r band
and 18.9 in z band. It is obvious that their r − i and i − z colors are too similar
to be distinguished using optical colors alone.
2. Selection of z∼5.5 Quasar Candidates
2.1. Using Optical and IR Colors to
Separate Quasars and M Dwarfs
At z ∼ 5, most quasars are undetectable in u-band and g-band
because of the presence of Lyman limit systems (LLSs), which
are optically thick to the continuum radiation from the quasar
(Fan et al. 1999). Meanwhile, Lyman series absorption systems
begin to dominate in the r band, and Lyα emission moves to the
i-band. The r−i/i−z color–color diagram is often used to
select z ∼ 5 quasar candidates in previous studies (Fan
et al. 1999; Richards et al. 2002; McGreer et al. 2013). At
higher redshift, the i−z color becomes redder, and most z > 5.1
quasars begin to enter the M dwarf locus in the r−i/i−z
color–color diagram, which makes it very difficult to select
z  5.2 quasars only with optical colors, especially at z ∼ 5.5,
where quasars have essentially the same optical colors as M
dwarfs (see Figure 1). Previous selections focused on the region
in the right-bottom of the r−i/i−z diagram.
In Figure 1, we plot the quasar color track from z=5 to
z=6 in the r−i/i−z color–color diagram, comparing with
locus of M dwarfs, and also show the comparison between
spectra of z ∼ 5.5 quasar and a typical M5 dwarf. As shown,
from earlier type to later type, M dwarfs show a redder r−i
color. M dwarfs earlier than M3 have a bluer r−i color than
quasars and can be more easily rejected. M dwarfs later than
M4 have their continua peak at 8000–12000 Å (Kirkpatrick
et al. 1993; McLean et al. 2003) and can seriously contaminate
quasar selection. Quasars at 5.3 „ z „ 5.7 have strong Lyα
emission line at 7600–8150 Å and a power-law continuum
redward of Lyα emission with an average index of αν=−0.5.
The locus of late-type M dwarfs (M4–M8) almost overlap the
whole region of z ∼ 5.5 quasars.
Wang et al. (2016) proposed a new color selection criteria
for z∼5 quasars, by adding photometric data from Wide Field
Infrared Survey Explorer (WISE), and at the same time relaxing
the r−i/i−z color cuts. ALLWISE W1–W2 color of
2
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
Figure 2. Color–color selection for 5.3 „ z „ 5.7 quasars based on r, i, z, J, H, K, W1, and W2 bands, and photometric data from SDSS, ULAS, and ALLWISE. We
modified the traditional r−i/i−z color cuts for quasars at z ∼ 5 to cover quasars at z ∼ 5.5 and add J, H, K, and W1 and W2 data. Blue dots denote simulated
quasars at 5.3 „ z„5.7 and SDSS z band brighter than 20.8, which is the 5σ magnitude limit of the SDSS z band. Gray dots show locus of SDSS Data Release 10
(DR10) spectroscopically identified M dwarfs. Our identified z ∼ 5.5 quasars (red solid circles) and lower redshift quasars (pink solid circles) from our candidate
sample are also plotted. The purple dashed lines represent our selection criteria, compared with previous r−i/i−z selection pipelines for the z ∼ 5 quasar (Fan
et al. 1999; Richards et al. 2002; McGreer et al. 2013; Wang et al. 2016, in brown, orange, cyan, and black dashed lines, respectively).
ALLWISE can separate z  5.1 quasars from M dwarfs due to
the redder W1–W2 color of high redshift quasars than that of
late-type stars. For quasars at z ∼ 5.5, if we include the whole
region overlapped by M dwarf locus on the r−i/i−z color–
color diagram, we will include a huge number of M dwarfs. In
this case, although using W1–W2 color can help reject a part of
M dwarfs, those remaining M dwarfs will still result in a high
contamination rate. More colors are needed to further reject M
dwarfs.
NIR photometry covering the wavelength range from 9000 Å
to 2 μm (J, H, K bands) will effectively distinguish z∼5.5
quasars from late-type M dwarfs. The spectral energy
distributions of z∼5.5 quasars are mainly dominated by a
power-law spectrum with a slope αν∼−0.5 at the wavelength
range from Lyα to Hβ, which is flatter than that of M dwarfs, a
gray body spectrum. So quasars have redder H−K, J−W1,
and K−W2 colors than that of M dwarfs. Especially at
z∼5.5, the Hα and Hβ emission lines in quasar spectra shift
to W2 and W1 bands, respectively. The J−W1, K−W2, and
even W1–W2 colors of quasars become redder and more
distinguishable form M dwarfs. We thus add J-W1, H−K,
and K −W2 colors, together with the riz color–color diagram
and W1–W2 color cut, to construct our new color selection
criteria of z∼5.5 quasars.
Figure 2 shows the loci of quasars and M dwarfs in various
color–color diagrams used in our new selection. For quasars, we
used synthetical r, i, z, J, H, K, W1, and W2 bands photometric
data from a sample of simulated quasar spectra at 5.3 „ z „5.7.
The details of this simulated quasar sample can be found in
Section 5.1. We used the same photometric systems as SDSS, the
UKIRT InfraRed Deep Sky Surveys (UKIDSS)–Large Area
Survey (ULAS) and WISE. For M dwarfs, we used the
spectroscopically identified M dwarfs from querying the SDSS
DR10 catalog, and obtained their NIR photometric data from
ULAS and ALLWISE. As shown, in the riz color–color diagram,
M dwarfs locate in a similar region as z ∼ 5.5 quasars. On the
other hand, on the J−W1/W1−W2 and H−K/K−W2
color diagrams, most of simulated quasars could be separated
from stars due to their redder J−W1, W1−W2, H−K, and
K−W2 colors. To improve the efficiency of selection and
reduce the size of candidate sample, we restrict our selection to
the cleanest regions (purple dashed lines in Figure 2), which are
still able to include most quasars.
2.2. Photometric Data Sets
We used the following photometric data sets to select
candidates of z∼5.5 quasars. In optical range, we used an
SDSS DR10 photometry catalog, which covered ∼14,400 deg2
with u, g, r, i, and z bands. In near-infrared wavelengths, the
published large area covering a deep NIR photometric database
with Y, J, H, and K(Ks) bands, are ULAS (Lawrence
et al. 2007) covering ∼4000 deg2 in the northern sky and
the VISTA Hemisphere Survey (VHS; McMahon et al. 2013)
covering the whole southern sky. To expand the available
survey area, although VHS focus on the southern sky, we also
did the selection in the overlap area between VHS, DR3, and
SDSS, an∼800 square degree field. For W1 and W2 bands, we
used the ALLWISE data set. The ALLWISE10 program
combined photometric data from the WISE cryogenic (Wright
et al. 2010) and NEOWISE (Mainzer et al. 2011) postcryogenic survey phases, and mapped the entire sky with W1,
W2, W3, and W4 (3.4, 4.6, 12, and 22 μm) bands. ALLWISE
have high detection completeness of known quasars over
almost all redshifts (Wang et al. 2016). Due to the shallower
detections in W3 and W4 bands, we only used W1 and
W2 data.
Therefore, we carried out a quasar survey for z ∼ 5.5 quasars
based on SDSS, ULAS/VHS, and ALLWISE photometric data
within a∼4800 deg2 field. The ALLWISE detection completeness of high redshift quasars (z>4.5) is 50% at z band
magnitude ∼20.5 (Wang et al. 2016) and decreases rapidly
toward the fainter end. ALLWISE W1 and W2, especially W2,
are not deep enough to detect z ∼ 5.5 quasars with SDSS z
band magnitude fainter than 20.5. We thus limited our selection
and main candidate sample with z < 20.5 mag.
2.3. Quasar Candidate Selection
We started the candidate selection from a catalog of
candidates that met our optical cuts, and selected only pointed
sources. We limited our selection area to galactic latitude
10
3
http://wise2.ipac.caltech.edu/docs/release/allwise/
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
−1
b > 20° or b<−20°, due to the quickly increasing star
contaminations at lower galactic latitude. The optical color
selection criteria we used for candidate selection are summarized as follows, and also shown in Figure 2. Here we did not
limit the signal to noise ratio (S/N) in r band, since r band may
be the drop-out band.
u > 22.3, g > 23.3, z < 20.5
grating centered at
telescope. We used the 270 lmm
7500 Å (8500 Å), providing coverage from ∼5700 to 9300 Å
(∼6700 to 10300 Å). We used the 1 0 or 1 5 slit, based on
seeing condition, providing resolutions of R∼640 and
R∼430, respectively.
We also used the Wide Field Spectrograph (WiFeS; Dopita
et al. 2007, 2010), an integral-field double-beam image-slicing
spectrograph on the ANU 2.3 m Telescope at Siding Spring
Observatory, to observe seven of our quasar candidates. They
were observed using Grating R3000 on WiFeS, which gives a
resolution of R=3000 at wavelengths between 5300 Å and
9800 Å.
The Lijiang 2.4 m telescope is located at Lijiang Observatory, Yunnan Observatories, at the Chinese Academy of
Sciences (CAS). It is equipped with the Yunnan Faint Object
Spectrograph and Camera (YFOSC), which can take spectra
followed by photometric images with a very short switching
time. We used Grism 5 (G5), with dispersion of 185 Å/mm and
wavelength coverage from 5000 to 9800 Å. We used a 1 8 slit,
which yields a resolution of R∼550.
Some candidates were observed using the Boller and
Chivens Spectrograph (B&C) on Steward Observatory’s
2.3 m Bok Telescope at Kitt Peak, with the G400 Grating
and 2 5 slit, which gives a resolution of R∼450 and ∼3400 Å
wavelength coverage.
All spectra taken on the 2.4 m telescope, 2.3 m Bok
telescope, and MMT telescope were reduced using standard
IRAF routines. The WiFeS data were reduced with a python
based pipeline PyWiFeS (Childress et al. 2014). The flux of all
spectra were calibrated using standard stars observed on the
same night and then scaled to SDSS i-bands magnitudes for
absolute flux calibration.
(1 )
S N (i) > 3, S N (z) > 3
(2 )
g > 24.0 or g - r > 1.8
(3 )
r - i > 1.3
(4 )
0.5 < i - z < 2.2
(5 )
We limited r−i/i−z colors and required drop-outs in u
and g bands to ensure the redshift range of quasars. We then
cross-matched optical selected objects with ULAS DR10 and
VHS DR3 and ALLWISE data using a 2″ cross radius. We
required objects to be detected in r, i, z, J, H, K, W1, and W2
bands. We selected sources that meet our SDSS-ULAS/VHSALLWISE J−W1/W1−W2 and H−K/K−W2 color
cuts. A J−K color cut was added here for further rejection of
stars. To ensure the W1 and W2 photometry quality, we limited
the signal-to-noise ratio to be higher than 5 in W1 and 3 in W2.
We did not further limit the S/N in J, H, K bands, as the
photometry in these bands are deep enough. Actually, all
sources that met our selection had S/N>3 in J, H, and
Kbands. The NIR color selections we used are listed as follows
(also in Figure 2).
S N (W1)  5, S N (W2)  3
(6 )
J - W1 > 1.5
(7 )
W1 - W2 > 0.5
(8 )
W1 - W2 > - 0.5 ´ (J - W1) + 1.4
(9 )
J - K > 0.8
(10)
H - K > 0.35 and K - W2 > 1.3
(11)
We visually checked images and removed targets with
suspicious detections, such as multiple peaked objects, or those
being affected by bright stars. The effect on color selection from
the difference between ULAS and VHS photometry systems,
especially in the K(Ks) band, is much smaller than photometric
uncertainties. Thus we used the same color cuts for ULAS and
VHS detected objects. Using this selection method, we selected
∼1000 quasar candidates in total as our main sample.
4. A New Sample of z∼5.5 Quasars
From our SDSS-ULAS/VHS-ALLWISE selected candidate
sample, we have observed 93 candidates and discovered 21
quasars. There are 15 new quasars in the redshift range of
5.3 „ z „ 5.7. Others are lower redshift quasars but all at
redshift z > 5, except one broad absorption line quasar with
z=4.50. There are also three quasars in our target list that
were already observed and published as z ∼ 5 candidates
(Wang et al. 2016). Two of them are z ∼ 5.5 quasars; the other
one is at z=5.24. Therefore, in the pilot observed sample, we
get a∼25% selection success rate for quasars and ∼18% for
the redshift range of 5.3 < z < 5.7. These quasars form a
uniformly selected sample of a z ∼ 5.5 quasar, with 17 quasars
in the magnitude limit of SDSS z=20.5. Most of the other 72
observed candidates are M dwarfs. Few of them can only be
ruled out as quasars, since there are no emission features, but
these cannot be identified further.
We measure the redshifts by visually matching the observed
spectrum to the quasar template using an eye-recognition assistant
for quasar spectra software (ASERA; Yuan et al. 2013). The
matching is based on Lyb , Lya, N V, O I/Si II, and Si IV emission
lines. The typical uncertainty of our redshift measurement is
around 0.03 and will be smaller for higher S/N spectra. We do not
include the systematic offset of the Lyα emission line (e.g., Shen
et al. 2007), which is typically ∼500 km/s and much smaller than
the uncertainty of matching.
We calculate the absolute magnitude at rest-frame 1450 Å,
M1450, by fitting a power-law continuum of each observed
3. Spectroscopic Identifications
Optical spectroscopy for the identification of z∼5.5 quasar
candidates were carried out using several facilities: the 6.5 m
MMT telescope and the 2.3 m Bok Telescope in the United
States, the 2.3 m ANU telescope in Australia, and the Lijiang
2.4 m telescope (LJT) in China. Our observations started in the
fall of 2014. To date, we have observed 93 candidates from our
main sample based on their brightness, colors, and positions.
We have discovered 21 new quasars. Three quasars, J0155
+0415, J2207−0416, and J2225+0330, were also in our
candidate list, but had been observed and published earlier as
z ∼ 5 candidates (Wang et al. 2016). All information regarding
spectroscopic identifications for these 24 quasars is listed in
Table 1.
We observed about 50 candidates using the Red Channel
spectrograph (Schmidt et al. 1989) on the MMT 6.5 m
4
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
Table 1
Spectroscopic Information of Newly Identified z∼5.5 Quasars
Name
Redshift
Instrument
Exposure(s)
Grating
Slit
J010806.60+071120.6
J011353.75+055951.1
J082933.10+250645.6
J093523.31−020754.4
J095712.20+101618.5
J100614.61−031030.4
J102201.91+080122.2
J113308.78+160355.7
J113414.23+082853.3
J114706.41−010958.2
J114946.45+074850.6
J131720.78−023913.0
J131929.23+151305.0
J133556.24−032838.2
J151339.64+085406.5
J152712.86+064121.9
J214239.27−012000.3
J232536.64−055328.3
J233008.71+095743.7
J235124.31−045907.3
J235824.04+063437.4
5.53
5.00
5.35
5.32
5.14
5.55
5.30
5.61
5.69
5.31
5.66
5.25
4.50
5.67
5.47
5.57
5.61
5.22
5.30
5.25
5.32
19.57
20.45
19.67
20.14
19.61
20.00
19.12
19.71
20.31
19.23
20.30
20.08
20.05
18.89
19.89
19.95
20.31
19.14
19.78
19.61
19.54
z
SSO2.3 m/WiFeS
SSO2.3 m/WiFeS
MMT/Red
MMT/Red
LJT/YFOSC
SSO2.3 m/WiFeS
MMT/Red
MMT/Red
MMT/Red
MMT/Red
MMT/Red
SSO2.3 m/WiFeS
MMT/Red
LJT/YFOSC
MMT/Red
MMT/Red
MMT/Red
SSO2.3 m/WiFeS
SSO2.3 m/WiFeS
SSO2.3 m/WiFeS
SSO2.3 m/WiFeS
1800.0
1800.0
300.0
250.0
3000.0
3600.0
300.0
250.0
600.0
300.0
900.0
3600.0
900.0
2500.0
600.0
300.0
600.0
1200.0
1800.0
1500.0
1800.0
R3000
R3000
G270
G270
G5
R3000
G270
G270
G270
G270
G270
R3000
G270
G5
G270
G270
G270
R3000
R3000
R3000
R3000
1.0
1.0
1.0
1.5
1.8
1.0
1.5
1.5
1.5
1.0
1.0
1.0
1.0
1.8
1.0
1.0
1.5
1.0
1.0
1.0
1.0
2014
2014
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2014
Obsdate
J015533.28+041506.7
J220710.12−041656.2
J222514.38+033012.5
5.37
5.53
5.24
19.26
18.95
19.47
Bok/B&C
LJT/YFOSC
Bok/B&C
2400.0
2400.0
2400.0
R400
G5
R400
2.5
1.8
2.5
2014 Oct 28
2014 Oct 22
2014 Oct 19
Oct 15
Oct 15
Mar 14
Nov 06
Feb 18
May 15
Nov 06
Nov 06
Mar 16
May 10
Mar 13
May 12
Mar 13
Feb 14
May 09
May 10
Nov 06
Jul 20
Aug 15
Jul 20
Oct 16
Note. Three quasars, J0155+0415, J2207−0416, and J2225+0330, were also the targets in our candidate list and had been identified earlier as z ∼ 5 candidates
(Wang et al. 2016). Thus we list them here separately.
spectrum. We assume an average quasar UV continuum slope
of an = -0.5 (Vanden Berk et al. 2001), due to the fact that
our spectra do not cover a wide enough wavelength range for
direct slope measurement. We normalize the power-law
continuum to match the visually identified continuum windows
that contain minimal contribution from quasar emission lines
and sky OH lines. Those quasar spectra used for fitting are all
scaled by using their SDSS i-band magnitude. The uncertainties of power-law continuum fitting are much smaller than the
photometric errors; therefore the uncertainties of M1450 are
comparable to SDSS i-band photometric errors. The redshifts,
M1450, and photometric information of our new quasars are
listed in Table 2. In Figure 3, we plot the redshift distribution of
our new quasars, compared with all previously known quasars
and SDSS-ULAS/VHS-ALLWISE detected known quasars in
the magnitude limit z < 20.5 mag. Our discoveries, including
the two that have been published as z ∼ 5 quasars, almost
double the number of known quasars at z ∼ 5.5, with z band
magnitude brighter than 20.5. All spectra of new quasars are
presented in Figure 4.
quasar with redder colors than normal lower redshift quasars;
this is why it could be selected by our selection.
J113414.23+082853.3, z=5.69. This one is the highest
redshift quasar in this sample, with strong Lyα emission and
strong IGM absorption blueward of Lyα.
5. Discussion
5.1. Selection Completeness
The sample obtained now is not yet a complete survey
sample, since we have only spectroscopically observed a small
part of candidates, in which case it is hard to estimate the
incompleteness of spectroscopy. But we want to first calculate
completeness to demonstrate the effectiveness of the selection
here. It will be used in later works. To first calculate the
completeness of our color selection criteria, we generate a
sample of simulated quasars using the quasar model from
McGreer et al. (2013). We extend this model toward redder
wavelengths to cover the ALLWISE W1, W2 bands for quasars
at z=5 to 6 (I. D. McGreer et al. 2017, in preparation; Yang
et al. 2016). Based on this model, a total of ∼200,000
simulated quasars have been generated and evenly distributed
in the (M1450, z) space of 5<z<6 and -30 < M1450 < -25.
We assign optical photometric errors using the magnitude-error
relations from the SDSS main survey. For J, H, and K bands,
we use the ULAS photometric errors. The magnitude-error
relations between ULAS and VHS are similar in the flux limit
of our selection. The little differences, including the difference
between ULAS K and VHS Ks, will not affect the selection
function too much. Here, to show the redshift and magnitude
dependent selection completeness, we only use the relations
4.1. Notes on Individual Objects
J133556.24–032838.2, z=5.67. It is one of the most
luminous new quasars, with M1450=−27.76.
J152712.86+064121.9, z=5.57. It is a weak line quasar
with a very weak Lyα emission line and no other obvious
emission features. We measure the redshift by matching the
continuum to template and the redshift uncertainty is a little
larger than others.
J131929.23+151305.0, z=4.50. This quasar has the lowest
redshift in our new discoveries. It is a broad absorption line
5
6
Name
Redshift
M1450
r
i
z
J
H
K
W1
W2
J010806.60+071120.6
J011353.75+055951.1
J082933.10+250645.6
J093523.31−020754.4
J095712.20+101618.5
J100614.61−031030.4
J102201.91+080122.2
J113308.78+160355.7
J113414.23+082853.3
J114706.41−010958.2
J114946.45+074850.6
J131720.78−023913.0
J131929.23+151305.0
J133556.24−032838.2
J151339.64+085406.5
J152712.86+064121.9
J214239.27−012000.3
J232536.64−055328.3
J233008.71+095743.7
J235124.31−045907.3
J235824.04+063437.4
5.53
5.00
5.35
5.32
5.14
5.55
5.30
5.61
5.69
5.31
5.66
5.25
4.50
5.67
5.47
5.57
5.61
5.22
5.30
5.25
5.32
−27.19
−25.83
−26.98
−26.25
−26.75
−26.96
−27.63
−27.49
−26.41
−27.44
−26.40
−26.27
−25.62
−27.76
−26.81
−26.92
−26.24
−27.13
−26.75
−26.34
−27.26
22.14±0.17
22.72±0.22
22.30±0.16
22.61±0.21
21.90±0.11
22.96±0.38
21.31±0.07
22.45±0.24
25.05±0.68
21.32±0.04
23.45±0.47
22.20±0.17
22.45±0.15
22.70±0.25
22.15±0.12
22.96±0.28
23.60±0.70
21.17±0.07
22.07±0.14
22.10±0.14
21.69±0.09
20.45±0.06
21.14±0.09
20.29±0.04
20.94±0.07
20.25±0.04
20.98±0.09
19.78±0.03
21.13±0.10
21.41±0.10
19.86±0.03
21.52±0.09
20.80±0.07
20.72±0.05
20.34±0.05
20.76±0.06
21.35±0.08
21.65±0.20
19.78±0.03
20.45±0.05
20.47±0.05
20.14±0.04
19.57±0.09
20.45±0.17
19.67±0.08
20.14±0.14
19.61±0.11
20.00±0.14
19.12±0.05
19.71±0.12
20.31±0.13
19.23±0.04
20.30±0.14
20.08±0.14
20.05±0.09
18.89±0.04
19.89±0.09
19.95±0.10
20.31±0.30
19.14±0.06
19.78±0.10
19.61±0.09
19.54±0.08
18.18±0.05
19.30±0.14
18.77±0.07
19.10±0.10
18.96±0.11
18.81±0.08
18.11±0.04
18.74±0.09
19.43±0.10
18.00±0.07
19.48±0.11
19.42±0.16
19.38±0.08
17.76±0.03
19.02±0.07
18.72±0.07
19.30±0.13
18.17±0.06
18.73±0.09
19.14±0.17
18.58±0.11
17.62±0.07
18.35±0.13
18.13±0.13
18.47±0.11
18.23±0.10
17.94±0.07
17.66±0.06
18.25±0.10
18.47±0.09
17.49±0.05
18.73±0.14
18.75±0.14
18.62±0.15
17.19±0.06
18.40±0.08
18.11±0.08
18.67±0.16
17.68±0.09
17.72±0.09
18.36±0.18
17.68±0.06
17.20±0.08
17.73±0.11
17.39±0.11
18.00±0.18
17.87±0.12
17.20±0.07
17.03±0.06
17.53±0.10
17.86±0.09
17.10±0.07
17.95±0.13
18.30±0.18
17.78±0.11
16.49±0.05
17.83±0.10
17.65±0.09
18.04±0.15
17.05±0.09
17.24±0.08
17.92±0.21
17.25±0.08
16.23±0.07
16.76±0.09
16.75±0.10
17.14±0.14
16.97±0.12
16.20±0.06
15.46±0.05
16.43±0.08
16.38±0.08
16.26±0.07
17.29±0.15
17.18±0.12
16.48±0.07
15.38±0.04
17.28±0.13
16.70±0.08
16.88±0.11
16.34±0.07
15.92±0.06
17.21±0.16
16.01±0.06
15.33±0.11
16.06±0.17
15.84±0.18
16.48±0.27
16.25±0.24
15.70±0.16
14.89±0.08
15.87±0.15
15.12±0.09
15.70±0.14
16.03±0.18
16.56±0.28
15.82±0.14
14.67±0.06
16.32±0.21
16.10±0.17
16.09±0.25
15.55±0.12
15.15±0.11
16.14±0.24
15.25±0.10
J015533.28+041506.7
J220710.12−041656.2
J222514.38+033012.5
5.37
5.53
5.24
−27.10
−27.77
−27.17
21.70±0.10
22.32±0.24
21.74±0.14
19.97±0.03
19.59±0.03
20.02±0.05
19.26±0.06
18.95±0.06
19.47±0.10
18.34±0.06
17.86±0.04
18.29±0.06
17.62±0.06
16.89±0.04
17.99±0.14
17.01±0.06
16.30±0.05
17.28±0.10
16.33±0.07
15.12±0.04
16.50±0.08
15.19±0.10
14.14±0.05
15.69±0.13
The Astronomical Journal, 153:184 (10pp), 2017 April
Table 2
Photometric Information of Newly Identified z∼5.5 Quasars
Yang et al.
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
area already covered by UKIDSS. The survey was begun by
the UKIDSS consortium, but is being completed by the new
operators of UKIRT (University of Arizona and University of
Hawaii). The data is initially proprietary but is intended to be
public in due course, through the same interface as UKIDSS
(i.e., the WFCAM Science Archive).11 The survey is briefly
described by Lawrence (2013) and will be fully reported in S.
Dye et al. (2016, in preparation). The combination of ULAS
and UHS will provide a complete J-band map in the northern
sky, with the depth matching SDSS.
To test our selection with UHS J-band, we selected a test
candidate sample in the spring sky. We cross-matched (2″) riz
selected candidates with the UHS and ALLWISE catalog. We
then used the same J − W1/W1–W2 color cut as discussed
previously, but without cuts related to H and K bands. Due to
the lack of H and K photometry, the fraction of M dwarf
contaminations will increase. Here we just focused on the
bright candidates with SDSS z < 19.5 to reduce the number of
candidates. We observed 9 selected candidates by using
Palomar P200/DBSP spectrograph and MMT/Red Channel
spectrograph in April and May 2016.
We discovered the first UHS J-band selected z ∼ 5.5 quasar
J101637.71+254131.912 atz=5.64. The spectrum was
obtained using P200/DBSP with grating G316 (R ∼ 960 at
7500 Å), 1 5 slit, and 500 s×3 exposure time on 2016 April
27 and April 30 (see the spectrum in Figure 4). Data was also
reduced by using standard IRAF routines. This quasar is a
luminous quasar with M1450=−27.81. We measure the
redshift and M1450 using the same method discussed in
Section 4.1. UHS J-band photometric data are helpful for the
large area z ∼ 5.5 quasar survey. If more NIR data will be
provided, we can significantly reduce the number of candidates,
such as using Pan-STARRS 1 (PS1) data (Chambers et al.
2016). PS1 covers the entire sky above decl. −30° in the g, r, i,
z, y filters. The area coverage and depth, especially in the
reddest narrower bands z and y, make PS1 a good choice for
z ∼ 5.5 quasar selection. Therefore with combined PS1 and
UHS data, a large uniform and completeness z ∼ 5.5 quasar
sample can be expected. Besides, the overlap area between PS1
and VHS will add a new optical/NIR covered area for quasar
selection in the Southern sky.
Figure 3. Left: distribution of all previously known quasars and our newly
discovered quasars with z band magnitude brighter than 20.5 at redshift
z > 4.9. Here we only count quasars within the flux limit of our survey. There
are also 11 previously known z∼5.5 quasars with z band magnitude fainter
than 20.5. As shown, there is an obvious gap at 5.1 < z < 5.7, especially at
z ∼ 5.5. We use the known quasar sample from the combination, with the
known quasar catalog in Wang et al. (2016) and new results in Bañados et al.
(2016). To date, using SDSS-ULAS/VHS-WISE color–color selection, we
have discovered 17 new quasars at 5.3 < z < 5.7 and 7 lower redshift quasars,
including 3 quasars that have been published in our z ∼ 5 quasar sample (Wang
et al. 2016; see also Tables 1 and 2). Our optical NIR color selection is
effective for finding quasars located in this redshift gap. Right: distribution of
our newly discovered quasars compared with all SDSS-ULAS/VHS-WISE (SU/V-W) detected known quasars (z < 20.5 mag).
from ULAS photometry. We will consider a separate VHS
photometry-based selection function in the further works for
complete sample construction and luminosity function measurement. ALLWISE detection depth is highly dependent on
sky position, which will affect the detection incompleteness
and photometric uncertainties. We model the coveragedependent detection incompleteness and photometric uncertainties of ALLWISE, using the ALLWISE coverage map
within the SDSS-ULAS/VHS area. We follow the procedure
outlined in Yang et al. (2016).
Figure 5 represents our selection function for z∼5.5
quasars. The selection function shows a high completeness in
the redshift range of 5.3 < z < 5.7, with a∼91% mean
completeness at M1450 < -26.5. In the range of
-26 < M1450 < -26.5, the mean selection probability is
around 40%. Toward the fainter end, the completeness
decreases quickly due to the increasing detection incompleteness and photometric uncertainties of ALLWISE W1 and W2
bands. As shown, the selected region with high completeness is
extended to the lower redshift to z ∼ 5.2. That is caused by the
slow evolution of quasars in r−i and i−z colors from
z=5.1 to 5.3 (see Figure 1). To include most of z ∼ 5.5
quasars, we use a relative relax riz cut, and thus can select some
lower redshift quasars, which is consistent with our result
shown in Figure 3. At high redshift end, our i−z < 2.2 cuts
will restrict the selected sample into z<5.7, due to the quick
increasing of quasar i−z color from z=5.7 to 5.8. So there is
a sharp edge at z=5.7.
6. Summary and Future Work
The obvious redshift gap of known quasars at 5.3 „ z„5.7
becomes a limitation of the study of IGM evolution, quasar
number density, and BH evolution from higher redshift to
lower redshift over the post-reionaiztion epoch. This gap is
caused by the same colors of z ∼ 5.5 quasar and late-type stars
in broad optical bands. To explore quasars at this redshift
range, we develop a new selection method for z ∼ 5.5 quasars
and build the first sample of quasars at 5.3 „ z „ 5.7. Main
results of our works are listed as follows.
1. In addition to the traditional r−i/i−z color–color
diagram, we add new color–color selection criteria based
on ULAS/VHS J, H, K, and ALLWISE W1&W2 bands.
We have done a pilot survey for z ∼ 5.5 quasars with
5.2. SDSS-UHS-ALLWISE Color Selection
Our selection using ULAS/VHS photometric data is limited
in a small area. To study the IGM and quasar number density, a
larger sample is required. Therefore the coming larger area
optical/NIR surveys will provide a good opportunity for
z ∼ 5.5 quasar selection. We used data from a preliminary
version of the UHS. This is a J-band survey of the northern sky
(0°<decl.<60°) to a depth of J=19.6, supplementing the
11
http://surveys.roe.ac.uk/wsa/
The photometric information of J1016+2541 is r=22.53±1.05,
i=20.33±0.05,z=18.96±0.05,J=17.87±0.05,
W1=15.67±0.05, and W2=14.99±0.08.
12
7
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
Figure 4. Spectra of our 22 new discovered quasars. Twenty-one are from our main SDSS-ULAS/VHS-ALLWISE selected z ∼ 5.5 quasar candidate sample. J1016
+2541 is from the UHS selected sample (Section 5.1). The blue vertical lines show the Ly β, Ly α, and Si IV emission lines. All spectra taken using SSO2.3 m/WiFeS
and P200/DBSP are smoothed with a 10 pixel boxcar. Spectra from MMT/Red and LJT/YFOSC are smoothed with a 3 pixel boxcar. All spectra are corrected for
Galactic extinction using the Cardelli et al. (1989) Milky Way reddening law and E(B−V ) derived from the Schlegel et al. (1998) dust map.
8
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
et al. 2012; DiPompeo et al. 2015). The extreme deconvolution
method in DiPompeo et al. (2015) required a sample of quasars
used as a training set. While at z∼5.5, there were few known
quasars, and most known quasars were located in the rightbottom region in the r−i/i−z diagram due to selection
criteria. There was no good training sample representing typical
colors of z∼5.5 quasars before. Our new quasar sample and
selection method could provide a new training sample for
future probabilistic selection. Besides, an extension of the
Bayesian model from Mortlock et al. (2012) to include more
NIR colors will also be expected to be useful for z ∼ 5.5 quasar
selection. Additionally, in the future, the variability (e.g.,
LSST) will also play an important role on high redshift quasar
selection.
We thank the referee for providing helpful comments and
suggestions. J. Yang, X.-B. Wu, and F. Wang acknowledge the
support from NSFC grant no. 11373008 and 11533001, the
Strategic Priority Research Program “The Emergence of
Cosmological Structures” of the Chinese Academy of Sciences,
grant no. XDB09000000, the National Key Basic Research
Program of China 2014CB845700, and the Ministry of Science
and Technology of China under grant 2016YFA0400703. J.
Yang, X. Fan, and I. D. McGreer acknowledge the support
from the U.S. NSF grant AST 11-07682 and AST 15-15115.
Funding for the Lijiang 2.4 m telescope is provided by the
Chinese Academy of Sciences and the People’s Government of
Yunnan Province. This research uses data obtained through the
Telescope Access Program (TAP), which has been funded by
the Strategic Priority Research Program “The Emergence of
Cosmological Structures” (grant no. XDB09000000), National
Astronomical Observatories, Chinese Academy of Sciences,
and the Special Fund for Astronomy from the Ministry of
Finance in China. We acknowledge the use of the Lijiang 2.4 m
telescope, the MMT 6.5 m telescope, the Bok telescope, the
ANU 2.3 m telescope, and the Palomar Hale 5 m telescope.
Observations obtained with the Hale Telescope at Palomar
Observatory were obtained as part of an agreement between the
National Astronomical Observatories, Chinese Academy of
Sciences, and the California Institute of Technology. This work
was partially supported by the Open Project Program of the
Key Laboratory of Optical Astronomy, National Astronomical
Observatories, Chinese Academy of Sciences.
We acknowledge the use of SDSS photometric data.
Funding for SDSS-III has been provided by the Alfred P.
Sloan Foundation, the participating institutions, the National
Science Foundation, and the U.S. Department of Energy Office
of Science. The SDSS-III website is http://www.sdss3.org.
SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III
Collaboration, including the University of Arizona, the
Brazilian Participation Group, Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University,
University of Florida, the French Participation Group, the
German Participation Group, Harvard University, the Instituto
de Astrofisica de Canarias, the Michigan State/Notre Dame/
JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for
Astrophysics, Max Planck Institute for Extraterrestrial Physics,
New Mexico State University, New York University, Ohio
State University, Pennsylvania State University, University of
Portsmouth, Princeton University, the Spanish Participation
Figure 5. The selection function of SDSS-ULAS-ALLWISE color selections.
Red points represent new quasars from our main candidates sample, including
three quasars that have been published in our z∼5 quasar sample (Wang
et al. 2016). The mean selection probability at 5.3 < z < 5.7 is ∼91% at
M1450 < −26.5 and is ∼84% at M1450 < −26. The decreasing of probability is
caused by the increasing photometric uncertainties at the faint end, especially in
ALLWISE W2 bands.
SDSS z band magnitude brighter than 20.5, using our
new selection pipeline.
2. We have discovered 21 new quasars from our SDSSULAS/VHS-ALLWISE selected main candidate sample.
There are 15 new quasars in the redshift range of
5.3 „ z „ 5.7 and 5 quasars at redshift 5 < z < 5.3. The
other one is a broad absorption line quasar with z=4.50.
There are also 3 quasars in our target list but already
being observed as z ∼ 5 candidates (Wang et al. 2016).
Two of these are z ∼ 5.5 quasars. Therefore, we construct
the first uniform z ∼ 5.5 quasar sample with 17 quasars in
the magnitude limit of SDSS z < 20.5.
3. The selection function shows a high completeness at
M1450 < -26, which can be expected to provide a sample
of new z ∼ 5.5 quasars for measurement of quasar
luminosity function at this redshift gap.
4. For the further application of a wide field quasar survey
and to construct a larger sample, we have tried to
construct the selection pipeline using UHS J-band data.
From our SDSS-UHS-ALLWISE selected test sample,
we discovered the first UHS selected z ∼ 5.5 luminous
quasar.
In a subsequent paper, we will present the final complete
sample of our z ∼ 5.5 quasar survey and the first measurement
of quasar LF at this epoch. The evolution of quasar density
from z=5 to 6 will also be constrained. Our selection focuses
on luminous quasars, so the new quasar sample at this redshift
range provides a valuable data set to study the IGM evolution
in the tail of reionization. We will expand our selection to a
large survey area using a new data set such as the UHS, PS1,
and VLT Survey Telescope (VST) ATLAS (Shanks
et al. 2015). In this work, we only use a color box to select
z∼5.5 quasar candidates. Recently, several modern techniques, probabilistic selections based on the Bayesian model or
extreme deconvolution method, have been explored to search
for quasars at different redshift ranges (e.g., Mortlock
9
The Astronomical Journal, 153:184 (10pp), 2017 April
Yang et al.
Group, University of Tokyo, University of Utah, Vanderbilt
University, University of Virginia, University of Washington,
and Yale University. This publication makes use of data
products from the Wide Field Infrared Survey Explorer, which
is a joint project of the University of California, Los Angeles,
and the Jet Propulsion Laboratory/California Institute of
Technology, and NEOWISE, which is a project of the Jet
Propulsion Laboratory/California Institute of Technology.
WISE and NEOWISE are funded by the National Aeronautics
and Space Administration. We acknowledge the use of the
UKIDSS data, and the VISTA data.
Facilities: Sloan (SDSS), WISE, 2.4 m/YAO (YFOSC),
MMT (red channel spectrograph), Palomar P200/Caltech,
2.3 m/ANU (WiFeS), Bok/Steward Observatory (B&C).
Jiang, L., Fan, X., Annis, J., et al. 2008, AJ, 135, 1057
Jiang, L., McGreer, I. D., Fan, X., et al. 2016, ApJ, 833, 222
Kashikawa, N., Ishizaki, Y., Willott, C. J., et al. 2015, ApJ, 798, 28
Kirkpatrick, J. D., Kelly, D. M., Rieke, G. H., et al. 1993, ApJ, 402, 643
Komatsu, E., Dunkley, J., Nolta, M. R., et al. 2009, ApJS, 180, 330
Lawrence, A. 2013, ASSP, 37, 271
Lawrence, A., Warren, S. J., Almaini, O., et al. 2007, MNRAS, 379, 1599
Lupton, R. H., Gunn, J. E., & Szalay, A. S. 1999, AJ, 118, 1406
Mainzer, A., Bauer, J., Grav, T., et al. 2011, ApJ, 731, 53
Matute, I., Masegosa, J., Márquez, I., et al. 2013, A&A, 557, A78
McGreer, I. D., Jiang, L., Fan, X., et al. 2013, ApJ, 768, 105
McGreer, I. D., Mesinger, A., & D’Odorico, V. 2015, MNRAS, 447, 499
McLean, I. S., McGovern, M. R., Burgasser, A. J., et al. 2003, ApJ, 596, 561
McMahon, R. G., Banerji, M., Gonzalez, E., et al. 2013, Msngr, 154, 35
Mortlock, D. J., Patel, M., Warren, S. J., et al. 2012, MNRAS, 419, 390
Pâris, I., Petitjean, P., Aubourg, É., et al. 2014, A&A, 563, A54
Pâris, I., Petitjean, P., Ross, N. P., et al. 2016, arXiv:1608.06483
Richards, G. T., Fan, X., Newberg, H. J., et al. 2002, AJ, 123, 2945
Romani, R. W., Sowards-Emmerd, D., Greenhill, L., & Michelson, P. 2004,
ApJL, 610, L9
Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525
Schmidt, G. D., Weymann, R. J., & Foltz, C. B. 1989, PASP, 101, 713
Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, AJ, 139, 2360
Shanks, T., Metcalfe, N., Chehade, B., et al. 2015, MNRAS, 451, 4238
Shen, Y., Strauss, M. A., Oguri, M., et al. 2007, AJ, 133, 2222
Simpson, C., Mortlock, D., Warren, S., et al. 2014, MNRAS, 442, 3454
Stern, D., Djorgovski, S. G., Perley, R. A., de Carvalho, R. R., & Wall, J. V.
2000, AJ, 119, 1526
Trakhtenbrot, B., Netzer, H., Lira, P., & Shemmer, O. 2011, ApJ, 730, 7
Vanden Berk, D. E., Richards, G. T., Bauer, A., et al. 2001, AJ, 122, 549
Wang, F., Wu, X.-B., Fan, X., et al. 2016, ApJ, 819, 24
Willott, C. J., Albert, L., Arzoumanian, D., et al. 2010a, AJ, 140, 546
Willott, C. J., Delorme, P., Reylé, C., et al. 2010b, AJ, 139, 906
Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ,
140, 1868
Yang, J., Wang, F., Wu, X.-B., et al. 2016, ApJ, 829, 33
Yuan, H., Zhang, H., Zhang, Y., et al. 2013, A&C, 3, 65
References
Bañados, E., Venemans, B. P., Decarli, R., et al. 2016, ApJS, 227, 11
Becker, G. D., Bolton, J. S., Madau, P., et al. 2015, MNRAS, 447, 3402
Bolton, J. S., Becker, G. D., Raskutti, S., et al. 2012, MNRAS, 419, 2880
Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245
Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2016, arXiv:1612.05560
Childress, M. J., Vogt, F. P. A., Nielsen, J., & Sharp, R. G. 2014, Ap&SS,
349, 617
Cool, R. J., Kochanek, C. S., Eisenstein, D. J., et al. 2006, AJ, 132, 823
DiPompeo, M. A., Bovy, J., Myers, A. D., & Lang, D. 2015, MNRAS,
452, 3124
Dopita, M., Hart, J., McGregor, P., et al. 2007, Ap&SS, 310, 255
Dopita, M., Rhee, J., Farage, C., et al. 2010, Ap&SS, 327, 245
Douglas, L. S., Bremer, M. N., Stanway, E. R., & Lehnert, M. D. 2007,
MNRAS, 376, 1393
Fan, X., Carilli, C. L., & Keating, B. 2006, ARA&A, 44, 415
Fan, X., Strauss, M. A., Schneider, D. P., et al. 1999, AJ, 118, 1
10